Solving a Class of Sum Power Minimization Problems by Generalized Water-Filling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Radio resource management (RRM) plays an important role in wireless communication systems, especially in more advanced systems with more constraint conditions. In this paper, we first propose a generalized water-filling approach to solve the power allocation problem of minimizing sum power while meeting the target sum rate constraint with weights. Based on this sum power objective function, we extend the proposed method to more complicated RRM problems with more stringent constraints. The proposed algorithms with this generalized approach possess several distinguished features. They provide exact optimal solutions based on non-derivative methods, as the implementation of the proposed algorithms invokes neither the derivative nor the gradient. With geometric interpretation, the proposed algorithms provide more insights into and intuitions of the problems and could be used to efficiently solve a family of the sum power minimization problems. Optimality of the proposed algorithms is strictly proved. Numerical results that illustrate the steps and demonstrate efficiency of the proposed algorithms are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it